In this recording of our most recent Technical Staff Meeting, we walk through our team’s work on Raster Vision, a set of open source tools for automatically analyzing aerial and satellite imagery using convolutional neural networks.

As part of Raster Vision, we have implemented approaches to tagging (predicts a set of tags for each image) and semantic segmentation (predicts the category of each pixel in an image). We’re also working on methods for object detection (localizes objects of interest in imagery).

Video Outline

a review of convolutional neural networks

our approaches to tagging and semantic segmentation for two machine learning contests

demo of a tool for visualizing output on an interactive map.

Seeing the results on a map can give a great sense of where the algorithms get it right, and where they get it wrong, and where they amusingly have a tough time figuring it out (e.g. a large food truck: is it a car or a building?)